Speaker Verification System Using LLR-Based Multiple Kernel Learning

Abstract

Support Vector Machine (SVM) has been shown powerful in pattern recognition problems. SVM-based speaker verification has also been developed to use the concept of sequence kernel that is able to deal with variable-length patterns such as speech. In this paper, we propose a new kernel function, named the Log-Likelihood Ratio (LLR)-based composite sequence kernel. This kernel not only can be jointly optimized with the SVM training via the Multiple Kernel Learning (MKL) algorithm, but also can calculate the speech utterances in the kernel function intuitively by embedding an LLR in the sequence kernel. Our experimental results show that the proposed method outperforms the conventional speaker verification approaches.